Stator Winding Of Induction Motor Research Articles

A new emotional intelligent adaptive system for controlling the proposed sensor-less induction motor drive with dual stator winding based on a ten-switch converter in low speed range

A dual stator winding induction motor (DSWIM) has two isolated three-phase windings in its stator, and its rotor is a usual squirrel cage type. In a sensor-less DSWIM drive, the standard and optimal operating mode in all operating speed ranges is the synchronous mode. Until now, researchers use the asynchronous mode to control it in the low speed range that is not the standard mode. But, this paper proposes a new intelligent model reference adaptive system (IMRAS) to control a DSWIM drive without the speed sensor based on a ten-switch converter in the low speed range. In the proposed intelligent sensor-less DSWIM drive, the rotor speed is estimated via an intelligent model as single and bi-objective functions. In the proposed IMRAS, the second objective increases the speed estimation accuracy at very low speeds. In this idea, the rotor speed error is used as data in the rotor speed estimation process. As a result, the convergence between estimated speed and reference speed in the proposed IMRAS is increased at zero and very low speeds. Also, in this paper, the power losses, the capital cost, and the number of switching elements in the converter are reduced via the concept of flux compensation and using a ten-switch converter. The intelligent proposed sensor-less DSWIM drive, unlike conventional methods, works in its standard operating mode (synchronous mode) and has a suitable performance in the low speed range. Hence, the converter power losses are significantly decreased. The suggested techniques are simulated in MATLAB software. The simulation of the proposed DSWIM drive system is performed under different scenarios of operating conditions and the obtained results approve the assumptions.

Sequence Component Based Approach to Detect Faults in Induction Motor Stator Winding

Sequence Component Based Approach to Detect Faults in Induction Motor Stator Winding

Numerical-field analysis of the magnetic conductivity of the slot dispersion of the stator winding of induction motors

Introduction. The magnetic fields and the corresponding magnetic conductivities of the slot dispersion of the stator winding of three-phase induction motors (IM) are studied. Such studies are relevant in their design and are one of the bases for calculating a number of energy parameters and characteristics of motors. In the classical design of an IM, which is still valid, the specified magnetic conductivities are determined analytically according to the simplified slot dispersion model, therefore they may not always provide sufficient accuracy due to a number of assumptions and conventions. Goal. Further development of the IM design system by means of a numerical-field calculation analysis of the magnetic conductivity of the slot dispersion of their stator winding, as well as a comparative check of the relevant analytical formulas from various methods of classical design. Results. The slot dispersion of IM is determined by numerical calculations using the FEMM program within the stator tooth steps. For the research, four variants of the shapes of the stator slots, which are common in modern IMs of small and medium power, are accepted. In the calculations, calculation models with strongly saturated and unsaturated in the magnetic relation with the stator core teeth are considered. The numerical-field method shows that the classical design method can give both sufficiently accurate results and unacceptable errors in determining the magnetic conductivities of slot dispersion of IM. Discussion and prospects for further development. The provided numerical-field analysis method and the obtained results of the calculation of slot dispersion of IM windings are recommended as a basis for improving the system of their design.

Induction Motor Stator Winding Inter-Tern Short Circuit Fault Detection Based on Start-Up Current Envelope Energy.

Inter-turn short circuit (ITSC) is a common fault in induction motors. However, it is challenging to detect the early stage of ITSC fault. To address this issue, this paper proposes an ITSC fault detection method for three-phase induction motors based on start-up current envelope energy. This approach uses Akima interpolation to calculate the envelope of the measured start-up current of the induction motor. A Gaussian window weighting is applied to eliminate endpoint effects caused by the initial phase angle, and the enveloping energy is obtained using the energy formula as the fault feature. Finally, by combining this with the support vector machine (SVM) classification learner, fault detection of ITSC in induction motors is achieved. The experimental results show that the average accuracy of this method reaches 96.9%, which can quickly and accurately detect ITSC faults in asynchronous motors and determine the severity of the faults. Furthermore, the average accuracy of SVM in detecting early ITSC faults under no-load conditions is 98.8%, which is higher than other classification learners, including LR, KNN, and NN. This study provides a new idea for induction motor fault detection and can contribute to induction motor maintenance.

A Proposed Fault Diagnostics Technique for Induction Motor Stator Winding

Online monitoring is widely used for induction motor fault diagnostics. This article presents a fault diagnostics technique for a 3-phase induction machine. The proposed technique was developed with fuzzy logic applied to a simplified induction motor model affected by the stator winding short turns. Based on the 3-phase time-domain model, the machine winding with different fault conditions has been simulated to check the resulting speed, torque, and stator current spectrum in each case. The results indicate that the developed fault diagnostics scheme is efficient to specify the fault type of the induction machines stator.

ASINXRON MOTOR REAKTIV QUVVATINI NAZORAT QILISH VA BOSHQARISHDA QO‘LLANILADIGAN TOK O‘ZGARTKICHNING DINAMIK TAVSIFLARI

This paper describes the use of a current transformer to monitor and control the reactive power of an induction motor. The current transformer is placed between the induction motor stator windings and the main winding. The result is a voltage signal due to the main and stray magnetic currents generated in the induction motor stator winding. The study of dynamic properties of the signal received from the measuring instrument, taking into account the interaction of various specific quantities, differential equations describing the transient processes in the primary and secondary sections of the transformer signal change, signal transmission elements and due to the difficulty in formation, is conducted on the basis of the developed mathematical apparatus - the graph model and its analytical expressions. The main element of the current transformer is the measuring coil, i.e. sensitive element. Depending on the number of slots of the induction motor stator the sensing element is made in the form of two- or three-ring. The measuring accuracy, reliability and sensitivity are increased compared to an independent measuring device. The sensor rings are connected independently or in series. When measuring circuits are connected in series, the output voltage value doubles. Influence of asynchronous motor on primary stator current at independent and series connection of gauge rings is obtained by means of dynamic descriptions.

Application of Frequency Response Analysis Method to Detect Short-Circuit Faults in Three-Phase Induction Motors

The industry has widely accepted Frequency Response Analysis (FRA) as a reliable method to detect power transformers mechanical deformations. While the FRA technique has been recommended in recent literature as a potential diagnostic method to detect internal faults within rotating machines, detailed feasibility studies have not been fully addressed yet. This paper investigates the feasibility of using the FRA technique to detect several short circuit faults in the stator winding of three-phase induction motors (TPIMs). In this regard, FRA testing is conducted on two sets of induction motors with various short circuit faults. Investigated faults include short circuits between two phases, short circuit turns within the same phase, phase-to-ground, and phase-to-neutral short circuit. The measured FRA signatures are divided into three frequency ranges: low, medium, and high. Several statistical indicators are employed to quantify the variation between faulty and healthy signatures in each frequency range. Experimental results attest the feasibility of the FRA technique as a diagnostic tool to detect internal faults in rotating machines, such as induction motors.

Identification of Inter-Turn Short-Circuits in Induction Motor Stator Winding Using Simulated Annealing

This paper presents a method of inter-turn short-circuit identification in induction motors during load current variations based on a hybrid analytic approach that combines the genetic algorithm and simulated annealing. With this approach, the essence of the method relies on determining the reference matrices and calculating the distance between the reference matric values and the test matrix. As a whole, it is a novel approach to the process of identifying faults in induction motors. Moreover, applying a discrete optimization algorithm to search for alternative solutions makes it possible to obtain the true minimal values of the matrices in the identification process. The effectiveness of the applied method in the monitoring and identification processes of the inter-turn short-circuit in the early stage of its creation was confirmed in tests carried out for several significant state variables describing physical magnitudes of the selected induction motor model. The need for identification of a particular fault is related to a gradual increase in its magnitude in the process of the induction motor’s exploitation. The occurrence of short-circuits complicates the dynamic properties of the measured diagnostic signals of the system to a great extent.

Application of Genetic Algorithm for Inter-Turn Short Circuit Detection in Stator Winding of Induction Motor

This paper presents a new method of inter-turn short-circuit detection in cage induction motors. The method is based on experimental data recorded during load changes. Measured signals were analyzed using a genetic algorithm. This algorithm was next used in the diagnostics procedure. The correctness of fault detection was verified during experimental tests for various configurations of inter-turn short-circuits. The tests were run for several relevant diagnostic signals that contain symptoms of faults in an examined cage induction motor. The proposed algorithm of inter-turn short-circuit detection for various levels of winding damage and for various loads of the examined motor allows one to state the usefulness of this diagnostic method in normal industry conditions of motor exploitation.

Determining an additional diagnostic parameter for improving the accuracy of assessment of the condition of stator windings in an induction motor

This paper has proposed and substantiated the application of an additional diagnostic parameter for assessing the state of stator windings of induction motors during operation. The dependences of the values of phase shifts between phase currents and phase voltages have been obtained. These dependences showed that when an inter-turn short circuit occurs in the stator windings, the phase shifts are the same for all phases of the motor. That has made it possible to obtain the dependence of the change in phase shift on the change in the engine shaft rotation frequency. This study's result has established the dependence of the rates of change of the phase angle on the engine shaft rotation frequency for both one and two damaged phases with varying degrees of damage. When analyzing these dependences, it was found that with an increase in the number of damaged phases of the electric motor, the linear section of the dependences decreases. In addition, with an increase in the degree of phase damage, the angle of inclination of the linear sections of the characteristics decreases. That has made it possible to determine an additional parameter for diagnosing the place and degree of an inter-turn short circuit of the windings in an induction motor with a squirrel-cage rotor. The values of the additional parameter, termed by this paper's authors as a "phase criterion" can be used to assess the condition and degree of damage to the stator winding of induction motors. The values of the phase criteria for various types of damage were: when phase A is damaged by 90 %, ξ=0.634, (deg)2/(rpm)2; when phase A is damaged by 80 %, ξ=0.393, (deg)2/(rpm)2; when phase A is damaged by 80 % and phase B is damaged by 90 %, ξ=0.25, (deg)2/(rpm)2; when phase A is damaged by 80 % and phase B is damaged by 90 %, ξ=0.173, (deg)2/(rpm)2. The results of this research could be used to select an effective method for diagnosing an inter-turn short circuit in the stator winding when building a diagnostic system for induction motors as part of drives of transport equipment

Двухскоростной асинхронный двигатель как источник опасности электропоражения в условиях эксплуатации технологических участков угольных шахт

Hazardous states of the mine zonal electrical network are caused by the reversed energy flows of induction motors of the energy-consuming equipment in the run-down mode after the power supply is switched off. The electromotive force (EMF) induced in the powered-off stator windings of the dual-speed motors due to the transformer effect also pose a danger of electric shock. The paper presents a methodology and the results of studying the formation of induced EMFs in the powered-off stator windings of dual-speed induction motors, including the run-down mode and the functions that impact on the electromagnetic parameters. Analysis of the impact degree of these induced EMFs on the electrical safety parameters as part of the mine zonal electrical network is presented.

Stator Faults Detection on Induction Motors Using Harmonic Sequence Current Components Analysis

In this paper we propose a strategy for detecting interturn short circuits on stator windings of three-phase induction motors (IM). The strategy is based on an analytical model that includes a stator fault in any of the phase windings considering the h-th harmonic in the supply voltage. To find the adequate model, we propose an equivalent electrical circuit of the IM in steady state, considering each sequence component. The proposed strategy allows the detection of an interturn short-circuit fault by monitoring the fifth-order harmonic component. We present simulation and experimental results for the faulty IM and different operating conditions. In the laboratory tests, we use a programmable power supply in order to synthesize distorted and unbalance voltages. Also, the windings of a three-phase IM have been modified to generate stator faults and validate the proposed model. The results show that the sustained increase of the fifth-order harmonic of positive-sequence current is a good indicator of the fault severity.

Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network

The paper deals with faults diagnosis method proposed to detect the inter-turn and turn to earth short circuit in stator winding of three-phase high-speed solid rotor induction motors. This method based on negative sequence current of motor and fuzzy neural network algorithm. On the basis of analysis of 2-D electromagnet field in the solid rotor the rotor impedance has been derived to develop the solid rotor induction motor equivalent circuit. The motor equivalent circuit is simulated by MATLAB software to study and record the data for training and testing the proposed diagnosis method. The numerical results of proposed approach are evaluated using simulation of a three-phase high-speed solid-rotor induction motor of two-pole, 140 Hz. The results of simulation shows that the proposed diagnosis method is fast and efficient for detecting inter-turn and turn to earth faults in stator winding of high-speed solid-rotor induction motors with different faults conditions

A Very High Resolution 30-Sided Space Vector Generation From a Single DC-Link for Induction Motor Drives

A voltage source inverter generating a polygonal voltage space vector structure (SVS) having 117- concentric 30-sided polygons of different radii are proposed. Modulation using a 30-sided SVS eliminates lower order harmonics till 25th order from motor phase voltage. The proposed scheme combines the advantages of a 30-sided SVS with that of a multilevel inverter. The topology consists of two cascaded five-level inverters feeding from either end of the open-end induction motor stator windings. One of the cascaded five-level inverter is connected to an active dc source and delivers the required active power for the scheme, while the other is capacitor fed and acts as a switched capacitive filter. In this article, nonaligned 30-sided polygons, i.e., regular 30-sided polygons that are not symmetric with respect to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> axis are also considered for inverter operation. The harmonic analysis of such nonaligned 30-sided polygons is investigated in this article. Nearest level switching is employed throughout the modulation range, enabling further reduction of switching loss in the system. Extensive hardware experimentations are carried out to validate the proposed scheme and the results are included in the article.

Inter-Turns Short Circuits in Stator Winding of Squirrel-Cage Induction Motor

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The Detection of Inter-Turn Short Circuits in the Stator Windings of Sensorless Operating Induction Motors

This work proposes an alternative strategy to the use of a speed sensor in the implementation of active and reactive power based model reference adaptive system (PQ-MRAS) estimator in order to calculate the rotor and stator resistances of an induction motor (IM) and the use of these parameters for the detection of inter-turn short circuits (ITSC) faults in the stator of this motor. The rotor and stator resistance estimation part of the IM is performed by the PQ-MRAS method in which the rotor angular velocity is reconstructed from the interconnected high gain observer (IHGO). The ITSC fault detection part is done by the derivation of stator resistance estimated by the PQ-MRAS estimator. In addition to the speed sensorless detection of ITSC faults of the IM, an approach to determine the number of shorted turns based on the difference between the phase current of the healthy and faulty machine is proposed. Simulation results obtained from the MATLAB/Simulink platform have shown that the PQ-MRAS estimator using an interconnected high-gain observer gives very similar results to those using the speed sensor. The estimation errors in the cases of speed variation and load torque are almost identical. Variations in stator and rotor resistances influence the performance of the observer and lead to poor estimation of the rotor resistance. The results of ITSC fault detection using IHGO are very similar to the results in the literature using the same diagnostic approach with a speed sensor.

RETRACTED: Research on motor control and simulation based on PID and Internet of Things system

A motor application consists of a Single Phase Induction Motor, and it has single-phase winding, which is winding on the stator of the motor and a winding placed on the rotor. A pulsating magnetic field is produced when a phase supply energizes the single-phase induction motor's stator winding shown below. In the existing method, DTC (Direct Torque Control) is based on converting alternating source and fed to the desired source motor low efficiency and high speed. So in this proposed method, Induction Motor Using Proportional-Integral-Derivative (PID) and IOT (Internet of Thing) for Inverter based achieving an efficiency output voltage or with the lowest amount of ripple content, the high switching frequency. Proportional Integral Derivative (PID) is an emerging technique for controlling PWM (Pulse Width Modulation) Inverter-Fed Induction Motor (IM) drives. The precise and quick inspection of the IM (Induction Motor) flux and torque without calling for complex control algorithms. In principle, moreover, it requires only the knowledge of the stator resistance. The application reviews an IM (Induction Motor) essential operation and a PWM (Pulse Width Modulation) inverter using the space vector theory. The IOT (Internet of Thing) control is a monitoring application of a Checking and controlling compliance and boundary is essential in many applications. The reliable functionality Policies available for equivalents and handles Observing different boundaries and three control.

Envelope Analysis on Vibration Signals for Stator Winding Fault Early Detection in 3-Phase Induction Motors

This paper brings up a novel method for detecting induction motor stator winding faults at an early stage. The contribution of the work comes from the delicate handling of motorvibration by applying envelope analysis, which makes it possible to capture electrical short-circuit signature in mechanical signals, even if the magnitude of the fault is fairly incipient. Conventional induction motor condition-based maintenance methods usually involve current and voltage measurements, which could be expensive to collect, and vibration-based analysis is often only capable of detecting the fault when it is already quite significant. In contrast, the solution presented in this study provides a refreshing perspective by applying time synchronous averaging to remove the discrete frequency component, and amplitude demodulation to further enhance the signal with the help of kurtogram. Experimental results on a three-phase induction motor show that the method is also able to distinguish different fault severity levels.

Frequency response analysis technique for induction motor short circuit faults detection

&lt;p&gt;The paper presents the description for diagnostic methods of induction motor's stator windings fault. The presented methods use Frequency Response Analysis (FRA) technique for detection of Winding Faults in Induction Motor . This method is previously reliable method for faults diagnosis and detection in many parts of transformers including transformer windings. In this paper, this method was used for motor windings faults detection. This paper presents the FRA response interpretation on internal short circuit (SC) fault at stator winding on three cases studies of different three-phase induction motors (TPIM), were analysed according to two status: healthy induction motor at normal winding status and same motor with windings shorted of main windings. A conclusion of this paper provides the interpretation of and validation the FRA response due to internal SC fault case by using NCEPRI algorithm, which is considered as one of certified statistical indicators. The proposed method in this paper had a useful result for detect and diagnosis of stator windings faults of TPIM. The applications of developed method can be used to detece the other machines types faults.&lt;/p&gt;

Identification and Classification of Stator Inter-Turn Faults in Induction Motor Using Wavelet Kernel Based Convolutional Neural Network

This paper presents an efficient technique for early diagnosis of simultaneous faults in different phases of stator winding of a three-phase induction motor due to turn-to-turn short circuit. A real-life motor has been designed and manufactured with fault emulation features in all the phases of stator winding. Phase currents are recorded by a data acquisition system for different fault conditions. Wavelet kernel-based convolutional neural network (WK-CNN) has been employed for identification and classification of the faults using the recorded current signatures. Various mother wavelets have been tested as convolution filters to extract salient features from the recorded current signatures followed by updating the weights of the filter at each epoch by a supervised learning algorithm. The reason to use a deep framework based on CNN is that it eliminates the requirement of feature extraction and classification algorithms separately. The proposed method also shows promising results when signals are contaminated by the noises, which is always a challenge in an industrial environment. Comparative results show the effectiveness of the proposed technique over the state-of-the-art methods.